HHS Public Access Author manuscript Author Manuscript
Am J Prev Med. Author manuscript; available in PMC 2017 November 01. Published in final edited form as: Am J Prev Med. 2016 November ; 51(5): e119–e127. doi:10.1016/j.amepre.2016.04.019.
Adult BMI and Access to Built Environment Resources in a HighPoverty, Urban Geography Elizabeth L. Tung, MD1, Monica E. Peek, MD, MPH1, Jennifer A. Makelarski, PhD, MPH2, Veronica Escamilla, PhD, MA2, and Stacy T. Lindau, MD, MAPP2,3,4 1Department
of Medicine, Section of General Internal Medicine, University of Chicago, Chicago,
Illinois
Author Manuscript
2Department
of Obstetrics and Gynecology, University of Chicago, Chicago, Illinois
3Department
of Medicine-Geriatrics, the MacLean Center on Clinical Medical Ethics, and the Comprehensive Cancer Center; University of Chicago, Chicago, Illinois
4Urban
Health Initiative at the University of Chicago Medicine, Chicago, Illinois
Abstract Introduction—The purpose of this study is to examine the relationship between BMI and access to built environment resources in a high-poverty, urban geography.
Author Manuscript
Methods—Participants (aged ≥35 years) were surveyed between November 2012 and July 2013 to examine access to common health-enabling resources (grocers, outpatient providers, pharmacies, places of worship, and physical activity resources). Survey data were linked to a contemporaneous census of built resources. Associations between BMI and access to resources (potential and realized) were examined using independent t-tests and multiple linear regression. Data analysis was conducted in 2014–2015. Results—Median age was 53.8 years (N=267, 62% cooperation rate). Obesity (BMI ≥30 kg/m2) prevalence was 54.9%. BMI was not associated with potential access to resources located nearest to home. Nearly all participants (98.1%) bypassed at least one nearby resource type; half bypassed nearby grocers (realized access >1 mile from home). Bypassing grocers was associated with a higher BMI (p=0.03). Each additional mile traveled from home to a grocer was associated with a
Author Manuscript
Address correspondence to: Elizabeth L. Tung, MD, 5841 South Maryland Avenue, MC 2007, Chicago IL 60637. [email protected]. Author contributions are as follows. Study concept and design: ELT and STL. Acquisition of data: JAM, MEP, and STL. Analysis and interpretation of data: all authors. Drafting of the manuscript: all authors. Critical revision of the manuscript for important intellectual content: all authors. Statistical analysis: ELT, VE, and JAM. Obtaining funding: STL. Administrative, technical, or material support: STL. Supervision: STL. Final approval of the version to be published: all authors. STL is founder and co-owner of a social impact company NowPow, LLC, developed as the sustainable business model expected by a Centers for Medicare and Medicaid Services Health Care Innovation Award (1C1CMS330997-03-00, 2012-15). This award did not directly support the research described in this manuscript. No other financial disclosures were reported by the authors of this paper. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Tung et al.
Page 2
Author Manuscript
0.9 kg/m2–higher BMI (95% CI=0.4, 1.3). Quality and affordability were common reasons for bypassing resources. Conclusions—Despite potential access to grocers in a high-poverty, urban region, half of participants bypassed nearby grocers to access food. Bypassing grocers was associated with a higher BMI.
Introduction
Author Manuscript
Racial and ethnic disparities in obesity and obesity-related disease are growing in the U.S.1,2 According to the Centers for Disease Control and Prevention, African American and Hispanic people are 50% more likely than non-minorities to be obese; non-Hispanic blacks have the highest age-adjusted rates of obesity (47.8%) compared with non-Hispanic whites (32.6%).1,2 These disparities have been attributed to residential segregation by race3,4 and growth in the geographic concentration of poverty in urban neighborhoods.5,6 Public health efforts to eliminate obesity-related disparities have thereby prompted a focus on the built environment, within high-poverty minority communities, as a potentially mutable factor.1
Author Manuscript
Defined as the physical spaces or structures created by people for day to day use,7 the built environment can enable people to maintain a healthy body weight or it can promote obesity. The availability of fresh food,8,9 recreational spaces,10 and community spaces11,12 has been associated with lower rates of obesity. Alternatively, fast food restaurants and decayed physical structures have been associated with higher rates of obesity.13 Other studies have found no association between the built environment and body weight.14,15 One longitudinal experiment followed obesity-related outcomes of public housing residents in five major U.S. cities over 14 years. People who were randomized at baseline with the opportunity to move from a neighborhood with high levels of poverty to a neighborhood with low levels of poverty were less likely to develop severe or morbid obesity.16 This finding suggests the possibility of a causal relationship between neighborhood characteristics and obesity among low-income people. Although prior studies have focused mainly on proximity to resources in the built environment as the driver of this relationship,17 little is known about how individual use of built resources relates to patterns of obesity. One prior study demonstrated that only one in seven participants reported shopping at the nearest supermarket,18 suggesting that most participants bypassed nearest supermarkets to access food. However, contrary to expectations, additional travel to a supermarket was not associated with a higher risk of obesity.18
Author Manuscript
Northridge and colleagues7 describe a multilevel ecosocial model to explain the pathways through which differential access to built resources can influence health. This model identifies the built environment as an intermediate factor that is particularly elastic to policy manipulation, including land use and community development strategies. Similarly, Andersen’s model19 identifies “enabling resources” as the only “highly mutable” factor in the pathway to health, in comparison with other characteristics (demographics, health beliefs) that may be less sensitive to external change. Andersen describes the presence of resources as “potential access,” and the use of resources as “realized access.” This study
Am J Prev Med. Author manuscript; available in PMC 2017 November 01.
Tung et al.
Page 3
Author Manuscript
applies concepts from both of these theoretic frameworks to describe potential versus realized access to enabling built resources.
Author Manuscript
This study uses primary individual data (including measured BMI) obtained from a population-based study and primary built environment data obtained from a contemporaneous census on Chicago’s South Side to describe the distribution of common health-enabling resources (grocers, outpatient providers, pharmacies, places of worship, physical activity resources) and how access to resources relates to BMI. Based on inconsistencies from prior studies, this study tests the null hypothesis that there is no systematic association between the presence of nearby enabling resources (potential access) and BMI within a high-poverty, urban region. Building on prior studies, it also retests the hypotheses18 that: (1) many people bypass nearby enabling resources to access more-distant enabling resources (realized access); and (2) the distance traveled to realize access is associated with a higher BMI.
Methods Study Design and Participants This study was conducted within a 62–square mile area on Chicago’s South Side, a densely populated (528,000) urban region with a high proportion of African American (77%) and Hispanic (13%) people living in poverty (55% 8,000 assets in the study region: 153.9 assets per 10,000 population including 1.2 pharmacies, 1.2 physical activity resources, 3.7 grocers, 4.1 outpatient clinics, and 17.6 places of worship.
Am J Prev Med. Author manuscript; available in PMC 2017 November 01.
Tung et al.
Page 4
Measures
Author Manuscript
Data on sociodemographic characteristics (age, self-identified race, ethnicity, gender, income, and education) were collected using items adapted from national surveys.24–26
Author Manuscript Author Manuscript
To capture “potential access” to resources, MAPSCorps data were used to identify the “nearest resource” for a subset of common enabling resource types identified as important levers by the Robert Wood Johnson Foundation Commission to Build a Healthier America,27,28 including access to health care (e.g., outpatient providers, pharmacies), access to healthy food (e.g., grocers), opportunities for physical activity (e.g., gyms, fitness classes), and promoting a culture of health in neighborhoods (e.g., places of worship). Although not the full range of resources identified by the Robert Wood Johnson Foundation framework, the resources included in this study are integrally linked, directly or indirectly, to a healthy environment that enables healthy body weight.27 It is important to acknowledge that some resources (e.g., pharmacies) may contain items that negatively influence body weight (e.g., junk food) in addition to the items that positively influence body weight (e.g. sports equipment, scales, fresh produce29). Using ArcGIS, version 10.1, Euclidian distance and driving time were calculated, in miles and minutes (at legal speed without traffic or stoplights), between the participant’s home and each nearest resource. Both distance and driving time were calculated because the survey did not elicit the mode of transportation used (e.g., walking, bus), which could influence access to each resource. U.S. Department of Agriculture walkability30 or convenient facilities31 criteria were applied to define a “nearby resource” as those resources located ≤1 mile or ≤5 minutes from the participant’s home, respectively. Definitions for “nearby” are based on different approaches in the prior literature,30,31 and are not equivalent (e.g., 1 mile does not equal 5 minutes). Applying Andersen’s behavioral model,19 nearest or nearby resources indicated “potential access” to resources; “bypassing” was defined as not using nearest or nearby resources relative to home (Appendix 1).
Author Manuscript
To assess the reasons for not using (bypassing) nearest resources, participants were asked a series of questions about the two grocers and two pharmacies that were located closest to their home, based on Euclidean distance. Participants were shown photographs of each grocer and pharmacy and asked: Do you know this place? Participants who reported knowing the place were asked: In the past 12 months, how often did you go to this place? Participants who reported never using the place were asked: Why don’t you buy most of your groceries/medicine here? Interviewers coded these open-ended responses as: poor quality, high price, can’t take the bus, too far, hours are inconvenient, or other. “Other” responses were later coded by the investigators using grounded theory and axial coding techniques. All responses were subsequently grouped into the following categories: poor quality, high price, preference, inconvenience, safety concerns, health system factors (e.g., lack of insurance), lack of awareness, or other. To develop internal consistency, coding was performed by two investigators and discrepancies were resolved by the research team. To capture “realized access” to resources, survey items were adapted from the Los Angeles Family and Neighborhood Survey.32 Survey participants were queried about the one place they usually go to exercise, see a doctor, buy medicine, and attend spiritual services. Participants were also asked about the two places they go to buy most of their groceries. Am J Prev Med. Author manuscript; available in PMC 2017 November 01.
Tung et al.
Page 5
Author Manuscript
Following the interview, MAPSCorps 2012 data were used to identify or verify the addresses of each “utilized resource,” and ArcGIS, version 10.1 to calculate Euclidian distance and driving time between the participant’s home and each utilized resource. Utilized resources indicated “realized access” to resources.19 Height and weight were measured at the time of interview to calculate BMI, using digital scales and measurement tapes based on a previously described protocol.33,34 BMI was calculated as mass/height2 × 703 kg/m2. Statistical Analysis
Author Manuscript
Participants with incomplete height or weight data were excluded from all analyses (4.5%). Overall missingness was low for all survey items (≤4.5%), with the exception of income (8.2%). Descriptive statistics were calculated for sociodemographic characteristics and to quantify distances and driving times to nearest and utilized resources. Independent t-tests were used to detect significant differences in BMI between those who bypassed and those who did not bypass nearby resources. Respondents were limited to those who had at least one nearby resource for each resource type. The distribution of reasons for bypassing the nearest grocer or pharmacy was described. Unadjusted and multiple linear regression models were used to evaluate relationships between BMI (continuous dependent variable) and measures of the built environment (continuous independent variables), including distance and time to each nearest and utilized resource type. For this analysis, if a participant reported using two grocers, the distance and time to each grocer were averaged, because participants reported using each grocer with a similar frequency distribution in the sample population (Appendix 2).
Author Manuscript Author Manuscript
All regression models adjusted for age and gender; other sociodemographic characteristics considered for inclusion were race, education, and income based on known relationships to the dependent variable.4,35 Self-reported health status,36 routine physical activity,37 and vehicle access,38 were also considered for inclusion, which have been identified in prior studies as related to the environment and BMI. Finally, duration of residence was considered for inclusion, because limited exposure to the neighborhood’s built environment would be expected to attenuate the relationship between the environment and BMI. Models were built for each primary independent variable of interest. Preliminary models included all covariates associated with BMI (p1 mile and those traveling ≤1mile, BMI was significantly higher among those bypassing grocery stores (p=0.02; Table 3). Comparing BMI between groups driving >5 minutes and those driving ≤5 minutes, BMI was significantly higher among those bypassing grocery stores (p=0.03), pharmacies (p=0.04), and places of worship (p=0.02; Table 3).
Author Manuscript
Respondents’ BMI was not associated with distance or driving time to nearest enabling resources (potential access; Table 4). However, BMI was associated with distance to utilized grocer (realized access) in both unadjusted and adjusted models. Each additional mile traveled to the utilized grocer was associated with a 0.9 kg/m2–higher BMI (p
Am J Prev Med. Author manuscript; available in PMC 2017 November 01. Published in final edited form as: Am J Prev Med. 2016 November ; 51(5): e119–e127. doi:10.1016/j.amepre.2016.04.019.
Adult BMI and Access to Built Environment Resources in a HighPoverty, Urban Geography Elizabeth L. Tung, MD1, Monica E. Peek, MD, MPH1, Jennifer A. Makelarski, PhD, MPH2, Veronica Escamilla, PhD, MA2, and Stacy T. Lindau, MD, MAPP2,3,4 1Department
of Medicine, Section of General Internal Medicine, University of Chicago, Chicago,
Illinois
Author Manuscript
2Department
of Obstetrics and Gynecology, University of Chicago, Chicago, Illinois
3Department
of Medicine-Geriatrics, the MacLean Center on Clinical Medical Ethics, and the Comprehensive Cancer Center; University of Chicago, Chicago, Illinois
4Urban
Health Initiative at the University of Chicago Medicine, Chicago, Illinois
Abstract Introduction—The purpose of this study is to examine the relationship between BMI and access to built environment resources in a high-poverty, urban geography.
Author Manuscript
Methods—Participants (aged ≥35 years) were surveyed between November 2012 and July 2013 to examine access to common health-enabling resources (grocers, outpatient providers, pharmacies, places of worship, and physical activity resources). Survey data were linked to a contemporaneous census of built resources. Associations between BMI and access to resources (potential and realized) were examined using independent t-tests and multiple linear regression. Data analysis was conducted in 2014–2015. Results—Median age was 53.8 years (N=267, 62% cooperation rate). Obesity (BMI ≥30 kg/m2) prevalence was 54.9%. BMI was not associated with potential access to resources located nearest to home. Nearly all participants (98.1%) bypassed at least one nearby resource type; half bypassed nearby grocers (realized access >1 mile from home). Bypassing grocers was associated with a higher BMI (p=0.03). Each additional mile traveled from home to a grocer was associated with a
Author Manuscript
Address correspondence to: Elizabeth L. Tung, MD, 5841 South Maryland Avenue, MC 2007, Chicago IL 60637. [email protected]. Author contributions are as follows. Study concept and design: ELT and STL. Acquisition of data: JAM, MEP, and STL. Analysis and interpretation of data: all authors. Drafting of the manuscript: all authors. Critical revision of the manuscript for important intellectual content: all authors. Statistical analysis: ELT, VE, and JAM. Obtaining funding: STL. Administrative, technical, or material support: STL. Supervision: STL. Final approval of the version to be published: all authors. STL is founder and co-owner of a social impact company NowPow, LLC, developed as the sustainable business model expected by a Centers for Medicare and Medicaid Services Health Care Innovation Award (1C1CMS330997-03-00, 2012-15). This award did not directly support the research described in this manuscript. No other financial disclosures were reported by the authors of this paper. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Tung et al.
Page 2
Author Manuscript
0.9 kg/m2–higher BMI (95% CI=0.4, 1.3). Quality and affordability were common reasons for bypassing resources. Conclusions—Despite potential access to grocers in a high-poverty, urban region, half of participants bypassed nearby grocers to access food. Bypassing grocers was associated with a higher BMI.
Introduction
Author Manuscript
Racial and ethnic disparities in obesity and obesity-related disease are growing in the U.S.1,2 According to the Centers for Disease Control and Prevention, African American and Hispanic people are 50% more likely than non-minorities to be obese; non-Hispanic blacks have the highest age-adjusted rates of obesity (47.8%) compared with non-Hispanic whites (32.6%).1,2 These disparities have been attributed to residential segregation by race3,4 and growth in the geographic concentration of poverty in urban neighborhoods.5,6 Public health efforts to eliminate obesity-related disparities have thereby prompted a focus on the built environment, within high-poverty minority communities, as a potentially mutable factor.1
Author Manuscript
Defined as the physical spaces or structures created by people for day to day use,7 the built environment can enable people to maintain a healthy body weight or it can promote obesity. The availability of fresh food,8,9 recreational spaces,10 and community spaces11,12 has been associated with lower rates of obesity. Alternatively, fast food restaurants and decayed physical structures have been associated with higher rates of obesity.13 Other studies have found no association between the built environment and body weight.14,15 One longitudinal experiment followed obesity-related outcomes of public housing residents in five major U.S. cities over 14 years. People who were randomized at baseline with the opportunity to move from a neighborhood with high levels of poverty to a neighborhood with low levels of poverty were less likely to develop severe or morbid obesity.16 This finding suggests the possibility of a causal relationship between neighborhood characteristics and obesity among low-income people. Although prior studies have focused mainly on proximity to resources in the built environment as the driver of this relationship,17 little is known about how individual use of built resources relates to patterns of obesity. One prior study demonstrated that only one in seven participants reported shopping at the nearest supermarket,18 suggesting that most participants bypassed nearest supermarkets to access food. However, contrary to expectations, additional travel to a supermarket was not associated with a higher risk of obesity.18
Author Manuscript
Northridge and colleagues7 describe a multilevel ecosocial model to explain the pathways through which differential access to built resources can influence health. This model identifies the built environment as an intermediate factor that is particularly elastic to policy manipulation, including land use and community development strategies. Similarly, Andersen’s model19 identifies “enabling resources” as the only “highly mutable” factor in the pathway to health, in comparison with other characteristics (demographics, health beliefs) that may be less sensitive to external change. Andersen describes the presence of resources as “potential access,” and the use of resources as “realized access.” This study
Am J Prev Med. Author manuscript; available in PMC 2017 November 01.
Tung et al.
Page 3
Author Manuscript
applies concepts from both of these theoretic frameworks to describe potential versus realized access to enabling built resources.
Author Manuscript
This study uses primary individual data (including measured BMI) obtained from a population-based study and primary built environment data obtained from a contemporaneous census on Chicago’s South Side to describe the distribution of common health-enabling resources (grocers, outpatient providers, pharmacies, places of worship, physical activity resources) and how access to resources relates to BMI. Based on inconsistencies from prior studies, this study tests the null hypothesis that there is no systematic association between the presence of nearby enabling resources (potential access) and BMI within a high-poverty, urban region. Building on prior studies, it also retests the hypotheses18 that: (1) many people bypass nearby enabling resources to access more-distant enabling resources (realized access); and (2) the distance traveled to realize access is associated with a higher BMI.
Methods Study Design and Participants This study was conducted within a 62–square mile area on Chicago’s South Side, a densely populated (528,000) urban region with a high proportion of African American (77%) and Hispanic (13%) people living in poverty (55% 8,000 assets in the study region: 153.9 assets per 10,000 population including 1.2 pharmacies, 1.2 physical activity resources, 3.7 grocers, 4.1 outpatient clinics, and 17.6 places of worship.
Am J Prev Med. Author manuscript; available in PMC 2017 November 01.
Tung et al.
Page 4
Measures
Author Manuscript
Data on sociodemographic characteristics (age, self-identified race, ethnicity, gender, income, and education) were collected using items adapted from national surveys.24–26
Author Manuscript Author Manuscript
To capture “potential access” to resources, MAPSCorps data were used to identify the “nearest resource” for a subset of common enabling resource types identified as important levers by the Robert Wood Johnson Foundation Commission to Build a Healthier America,27,28 including access to health care (e.g., outpatient providers, pharmacies), access to healthy food (e.g., grocers), opportunities for physical activity (e.g., gyms, fitness classes), and promoting a culture of health in neighborhoods (e.g., places of worship). Although not the full range of resources identified by the Robert Wood Johnson Foundation framework, the resources included in this study are integrally linked, directly or indirectly, to a healthy environment that enables healthy body weight.27 It is important to acknowledge that some resources (e.g., pharmacies) may contain items that negatively influence body weight (e.g., junk food) in addition to the items that positively influence body weight (e.g. sports equipment, scales, fresh produce29). Using ArcGIS, version 10.1, Euclidian distance and driving time were calculated, in miles and minutes (at legal speed without traffic or stoplights), between the participant’s home and each nearest resource. Both distance and driving time were calculated because the survey did not elicit the mode of transportation used (e.g., walking, bus), which could influence access to each resource. U.S. Department of Agriculture walkability30 or convenient facilities31 criteria were applied to define a “nearby resource” as those resources located ≤1 mile or ≤5 minutes from the participant’s home, respectively. Definitions for “nearby” are based on different approaches in the prior literature,30,31 and are not equivalent (e.g., 1 mile does not equal 5 minutes). Applying Andersen’s behavioral model,19 nearest or nearby resources indicated “potential access” to resources; “bypassing” was defined as not using nearest or nearby resources relative to home (Appendix 1).
Author Manuscript
To assess the reasons for not using (bypassing) nearest resources, participants were asked a series of questions about the two grocers and two pharmacies that were located closest to their home, based on Euclidean distance. Participants were shown photographs of each grocer and pharmacy and asked: Do you know this place? Participants who reported knowing the place were asked: In the past 12 months, how often did you go to this place? Participants who reported never using the place were asked: Why don’t you buy most of your groceries/medicine here? Interviewers coded these open-ended responses as: poor quality, high price, can’t take the bus, too far, hours are inconvenient, or other. “Other” responses were later coded by the investigators using grounded theory and axial coding techniques. All responses were subsequently grouped into the following categories: poor quality, high price, preference, inconvenience, safety concerns, health system factors (e.g., lack of insurance), lack of awareness, or other. To develop internal consistency, coding was performed by two investigators and discrepancies were resolved by the research team. To capture “realized access” to resources, survey items were adapted from the Los Angeles Family and Neighborhood Survey.32 Survey participants were queried about the one place they usually go to exercise, see a doctor, buy medicine, and attend spiritual services. Participants were also asked about the two places they go to buy most of their groceries. Am J Prev Med. Author manuscript; available in PMC 2017 November 01.
Tung et al.
Page 5
Author Manuscript
Following the interview, MAPSCorps 2012 data were used to identify or verify the addresses of each “utilized resource,” and ArcGIS, version 10.1 to calculate Euclidian distance and driving time between the participant’s home and each utilized resource. Utilized resources indicated “realized access” to resources.19 Height and weight were measured at the time of interview to calculate BMI, using digital scales and measurement tapes based on a previously described protocol.33,34 BMI was calculated as mass/height2 × 703 kg/m2. Statistical Analysis
Author Manuscript
Participants with incomplete height or weight data were excluded from all analyses (4.5%). Overall missingness was low for all survey items (≤4.5%), with the exception of income (8.2%). Descriptive statistics were calculated for sociodemographic characteristics and to quantify distances and driving times to nearest and utilized resources. Independent t-tests were used to detect significant differences in BMI between those who bypassed and those who did not bypass nearby resources. Respondents were limited to those who had at least one nearby resource for each resource type. The distribution of reasons for bypassing the nearest grocer or pharmacy was described. Unadjusted and multiple linear regression models were used to evaluate relationships between BMI (continuous dependent variable) and measures of the built environment (continuous independent variables), including distance and time to each nearest and utilized resource type. For this analysis, if a participant reported using two grocers, the distance and time to each grocer were averaged, because participants reported using each grocer with a similar frequency distribution in the sample population (Appendix 2).
Author Manuscript Author Manuscript
All regression models adjusted for age and gender; other sociodemographic characteristics considered for inclusion were race, education, and income based on known relationships to the dependent variable.4,35 Self-reported health status,36 routine physical activity,37 and vehicle access,38 were also considered for inclusion, which have been identified in prior studies as related to the environment and BMI. Finally, duration of residence was considered for inclusion, because limited exposure to the neighborhood’s built environment would be expected to attenuate the relationship between the environment and BMI. Models were built for each primary independent variable of interest. Preliminary models included all covariates associated with BMI (p1 mile and those traveling ≤1mile, BMI was significantly higher among those bypassing grocery stores (p=0.02; Table 3). Comparing BMI between groups driving >5 minutes and those driving ≤5 minutes, BMI was significantly higher among those bypassing grocery stores (p=0.03), pharmacies (p=0.04), and places of worship (p=0.02; Table 3).
Author Manuscript
Respondents’ BMI was not associated with distance or driving time to nearest enabling resources (potential access; Table 4). However, BMI was associated with distance to utilized grocer (realized access) in both unadjusted and adjusted models. Each additional mile traveled to the utilized grocer was associated with a 0.9 kg/m2–higher BMI (p
